Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

233
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
233
Dementia01:30

Dementia

150
Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
150
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

575
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
575

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Bilateral vocal cord paralysis in multifocal motor neuropathy.

BMJ case reports·2026
Same author

Cell type-specific contextualisation of the human phenome: towards the systematic treatment of all rare diseases.

Genome medicine·2026
Same author

The role of systemic microvascular function in the association between large artery stiffness and cognitive function in older adults.

Journal of hypertension·2026
Same author

Using GPT-4 to annotate the severity of all phenotypic abnormalities within the human phenotype ontology.

Frontiers in digital health·2026
Same author

AI-assisted teams outperform AI-led teams but not human-only teams in assessing research reproducibility in quantitative social science.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Classical HLA class II associations with ALS in Kuwait reveal a DR7-DQ2.2 risk haplotype.

Frontiers in immunology·2026

Related Experiment Video

Updated: Aug 7, 2025

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
06:46

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Published on: August 4, 2018

12.2K

Artificial Intelligence for Dementia Research Methods Optimization.

Magda Bucholc1, Charlotte James2, Ahmad Al Khleifat3

  • 1Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK.

Arxiv
|March 13, 2023
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) advances dementia research by identifying key features and improving diagnostic accuracy. Future ML applications promise to enhance clinical practice and accelerate the understanding of dementia

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Related Experiment Videos

Last Updated: Aug 7, 2025

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
06:46

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Published on: August 4, 2018

12.2K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

Area of Science:

  • Neuroscience
  • Computer Science
  • Medical Informatics

Background:

  • Machine learning (ML) demonstrates high accuracy in analyzing complex, high-dimensional data.
  • Dementia research increasingly utilizes ML for identifying disease biomarkers and patterns.

Approach:

  • This review critically evaluates current ML applications in dementia research.
  • It highlights frequently used ML algorithms and their potential in clinical settings.

Key Points:

  • ML aids in understanding dementia's causes and pathological mechanisms.
  • Addressing reproducibility, replicability, and interpretability is crucial for clinical translation.
  • Advanced ML techniques like transfer learning can overcome current challenges.

Conclusions:

  • ML models offer significant promise for advancing dementia research.
  • Future ML integration can improve early detection, diagnosis, and treatment strategies.
  • Enhanced ML approaches will facilitate the translation of research findings into clinical practice.